Skip to main content

A library for Reinforcement Learning

Project description

# anyrl-py

This is a Python remake (and makeover) of [anyrl]( It is a general-purpose library for Reinforcement Learning which aims to be as modular as possible.

# Installation

You can install anyrl with pip:

` pip install anyrl `

# APIs

There are several different sub-modules in anyrl:

  • models: abstractions and concrete implementations of RL models. This includes actor-critic RNNs, MLPs, CNNs, etc. Takes care of sequence padding, BPTT, etc.
  • envs: APIs for dealing with environments, including wrappers and asynchronous environments.
  • rollouts: APIs for gathering and manipulating batches of episodes or partial episodes. Many RL algorithms include a “gather trajectories” step, and this sub-module fulfills that role.
  • algos: well-known learning algorithms like policy gradients or PPO. Also includes mini-algorithms like Generalized Advantage Estimation.
  • spaces: tools for using action and observation spaces. Includes parameterized probability distributions for implementing stochastic policies.

# Motivation

Currently, most RL code out there is very restricted and not properly decoupled. In contrast, anyrl aims to be extremely modular and flexible. The goal is to decouple agents, learning algorithms, trajectories, and things like GAE.

For example, anyrl decouples rollouts from the learning algorithm (when possible). This way, you can gather rollouts in several different ways and still feed the results into one learning algorithm. Further, and more obviously, you don’t have to rewrite rollout code for every new RL algorithm you implement. However, algorithms like A3C and Evolution Strategies may have specific ways of performing rollouts that can’t rely on the rollout API.

# Use of TensorFlow

This project relies on TensorFlow for models and training algorithms. However, anyrl APIs are framework-agnostic when possible. For example, the rollout API can be used with any policy, whether it’s a TensorFlow neural network or a native-Python decision forest.

# Style

I use autopep8 and flake8. Here is the command you can use to run autopep8:

` autopep8 --recursive --in-place --max-line-length 100 . `

I recommend the following flag for flake8: –max-line-length=100

Project details

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for anyrl, version 0.12.23
Filename, size File type Python version Upload date Hashes
Filename, size anyrl-0.12.23-py3-none-any.whl (90.0 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size anyrl-0.12.23.tar.gz (65.7 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page